Analysing and Predicting Micro-Location Patterns of Software Firms

Abstract

Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. also show that non-aggregated data can be used to detect information on location determinants, which are superimposed when aggregated spatial units are analysed, and that some findings of previous firm location studies are not robust at the microgeographic level. However, we also conclude that the lack of high-resolution geodata on socio-economic population characteristics causes systematic prediction errors, especially in cities with diverse and segregated populations.

Location Data Analytics in the Business Value Chain: A Systematic Literature Review

“…Other data sources correspond to dedicated location information providers such as the Environmental Systems Research Institute (ESRI 1 ), Openstreetmap 2 , and Google [11], [12], [17], [24], [44], [45], [47]- [50], [52], [54], [55], [60], [64], [76], [78], [80]- [85], [87]- [93], [95]- [97], [107], [108], [111]- [114], [117], [118], [121], [126], [127], [129], [132], [137], [138], [143], [147], [149]- [154], [156], [157], [159], [168], [169], [171], [175]- [184], [192], [197], [199]...…”

“…As expected, economy data was mostly used in business performance related approaches. Some studies focused on external factors, such is the case of Teng et al [51], who analyzed the performance of multinational enterprise (MNE) subsidiaries using country wide economic indicators [171].…”

“…Content may change prior to final publication. [13], [44], [45], [47], [49]- [51], [53]- [56], [58], [60], [64], [76], [78], [82]- [84], [86]- [90], [93]- [98], [101], [102], [106]- [111], [113], [114], [116]- [120], [122]- [124], [127], [128], [132], [134]- [138], [143], [148], [149], [153], [155], [156], [158], [160], [161], [163], [167], [171], [175], [178]- [183], …”

“…The second most addressed problem type was spatial analysis, which represents 32.74% of the papers. All the studies that performed some level of descriptive analysis, inference, or observation of spatial properties of events used [13], [44], [45], [47]- [50], [52]- [55], [60], [64], [65], [76], [78], [80]- [91], [95]- [97], [101], [102], [104], [107]- [109], [111]- [115], [117], [118], [121], [126], [138], [143]- [148], [150]- [152], [155], [157], [159], [161], [163], [166]- [171], [174]- [184], [186], [192], [196], [198], [199] Spatial analysis 55 32.74% [11],…”

“…Depending on the model objective, there are some metrics that are more suitable than others. We identified the following metrics: 18.00% [23], [24], [28], [46], [58], [66]- [69], [71], [74], [129], [139], [141], [186], [188], [189], [194] Recall 18 18.00% [14], [23], [24], [28], [46], [58], [66]- [69], [71], [72], [74], [129], [139], [141], [186], [188] RMSE 11 11.00% [17], [25], [57]- [59], [62], [141], [158], [164], [171], [172] F1-score 10 10.00% [24], [28], [58], [66], [71],…”